Aggresome formation is regulated by RanBPM through an interaction with HDAC6
Bibliographic record
Abstract
In conditions of proteasomal impairment, the build-up of damaged or misfolded proteins activates a cellular response leading to the recruitment of damaged proteins into perinuclear aggregates called aggresomes. Aggresome formation involves the retrograde transport of cargo proteins along the microtubule network and is dependent on the histone deacetylase HDAC6. Here we show that ionizing radiation (IR) promotes Ran-Binding Protein M (RanBPM) relocalization into discrete perinuclear foci where it co-localizes with aggresome components ubiquitin, dynein and HDAC6, suggesting that the RanBPM perinuclear clusters correspond to aggresomes. RanBPM was also recruited to aggresomes following treatment with the proteasome inhibitor MG132 and the DNA-damaging agent etoposide. Strikingly, aggresome formation by HDAC6 was markedly impaired in RanBPM shRNA cells, but was restored by re-expression of RanBPM. RanBPM was found to interact with HDAC6 and to inhibit its deacetylase activity. This interaction was abrogated by a RanBPM deletion of its LisH/CTLH domain, which also prevented aggresome formation, suggesting that RanBPM promotes aggresome formation through an association with HDAC6. Our results suggest that RanBPM regulates HDAC6 activity and is a central regulator of aggresome formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".